Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning.

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第一作者： Nikalabh,Dihingia

第一单位： Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.

作者： Nikalabh,Dihingia ^[1] ; Gabriel A,Vázquez-Lizardi ^[1] ; Ryan J,Wu ^[2] ; Danielle,Reifsnyder Hickey ^[1]

作者单位： Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. ^[1] Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. ^[2] Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. ^[3]

DOI 10.1063/5.0188928

PMID 38436439

发布时间 2024-03-04

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The Journal of chemical physics

2024年160卷9期

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Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning.

The Journal of chemical physics

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Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning.

The Journal of chemical physics

相似文献

The Journal of chemical physics

相关期刊

检索历史清除